用户评论方面级情感分析研究

doi:10.3778/j.issn.1673-9418.2007011

摘要/Abstract

摘要：

方面级情感分析是自然语言处理的热门研究方向之一，相比于传统的情感分析技术，基于方面的情感分析是细粒度的，能够判断句子中多个目标的情感倾向，能更加准确地挖掘用户对目标的情感极性。针对以往研究忽略目标单独建模的问题，提出了一种基于双向长短期记忆神经网络（BiLSTM）的交互注意力神经网络模型（Bi-IAN）。该模型通过BiLSTM对目标和上下文分别进行建模，获得目标和上下文的隐藏表示，提取其中的语义信息。接下来利用交互注意模块学习上下文和目标之间的注意力，分别生成目标和上下文的表示，捕捉目标和上下文之内和之间的相关性，并重构评价对象和上下文的表示，最终通过非线性层得到分类结果。在数据集SemEval 2014任务4和Chinese review datasets上的实验训练显示，在正确率和F1-score上，比现有的基准情感分析模型有更好的效果。

关键词: 方面级情感分析, 深度学习, 循环神经网络（RNN）, 注意力机制

Abstract:

Aspect-based sentiment analysis has become one of the hot research directions of natural language processing. Compared with the traditional sentiment analysis technology, aspect-based sentiment analysis is aimed at specific targets in sentences, and can judge the sentiment tendency of multiple targets in a sentence, and more accurately mine the sentiment polarity of the target. It is a fine-grained sentiment analysis technology. Aiming at the fact that the previous research ignored the problem of separate modeling of targets, an interactive attention network model based on bidirectional long short-term memory (Bi-IAN) is proposed. The model uses bidirectional long short-term memory (BiLSTM) to model the targets and the context respectively, to obtain hidden representation and extract the semantic information. Next, the attention vector between the context and the targets is learnt through interactive learning, and then the representation of the target and the context are generated. The relevance within and between the target and the context is captured, the representation of the target and context is reconstructed, and finally the model gets the classification result through the non-linear layer. Experimental training on the dataset SemEval 2014 task 4 and Chinese review datasets shows that the model proposed has better results than the existing benchmark sentiment analysis model in terms of accuracy and F1-score.

Key words: aspect-level sentiment analysis, deep learning, recurrent neural network (RNN), attention mechanism

陈虹, 杨燕, 杜圣东. 用户评论方面级情感分析研究[J]. 计算机科学与探索, 2021, 15(3): 478-485.

CHEN Hong, YANG Yan, DU Shengdong. Research on Aspect-Level Sentiment Analysis of User Reviews[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(3): 478-485.

参考文献

[1] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. Semeval-2016 task 5: aspect based sentiment analysis[C]//Proceedings of the 10th International Workshop on Semantic Evaluation, San Diego, Jun 16-17, 2016. Stroudsburg: ACL, 2016: 19-30.
[2] WANG Z Y, CHENG J P, WANG H X, et al. Short text understanding: a survey[J]. Journal of Computer Research and Development, 2016, 53(2): 262-269.
王仲远, 程健鹏, 王海勋, 等. 短文本理解研究[J]. 计算机研究与发展, 2016, 53(2): 262-269.
[3] PANG B, LEE L, VAITHYANATHAN S. Thumbs up? sen-timent classification using machine learning techniques[C]//Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, Philadelphia, Jul 6-7, 2002. Stroudsburg: ACL, 2002: 79-86.
[4] LIU B W, BLASCH E, CHEN Y, et al. Scalable sentiment classification for big data analysis using naive Bayes class-ifier[C]//Proceedings of the 2013 IEEE International Con-ference on Big Data, Santa Clara, Oct 6-9, 2013. Washington: IEEE Computer Society, 2013: 99-104.
[5] LE Q V, MIKOLOV T. Distributed representations of sentences and documents[C]//Proceedings of the 31st International Conference on Machine Learning, Beijing, Jun 21-26, 2014: 1188-1196.
[6] TANG D Y, QIN B, FENG X C, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Ling-uistics, Osaka, Dec 11-16, 2016. Stroudsburg: ACL, 2016: 3298-3307.
[7] WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-based LSTM for aspect-level sentiment classification[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Nov 1-4, 2016. Str-oudsburg: ACL, 2016: 606-615.
[8] YANG M, TU W T, WANG J X, et al. Attention based LSTM for target dependent sentiment classification[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, Feb 4-9, 2017. Menlo Park: AAAI, 2017: 5013-5014.
[9] LIU J M, ZHANG Y. Attention modeling for targeted sen-timent[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Apr 3-7, 2017. Stroudsburg: ACL, 2017: 572-577.
[10] CHEN P, SUN Z Q, BING L D, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Pro-ceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Sep 9-11, 2017. Stroudsburg: ACL, 2017: 452-461.
[11] CAO Y, LI T R, JIA Z, et al. BGRU: new method of Chinese text sentiment analysis[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(6): 973-981.
曹宇, 李天瑞, 贾真, 等. BGRU:中文文本情感分析的新方法[J]. 计算机科学与探索, 2019, 13(6): 973-981.
[12] PENG H Y, MA Y K, LI Y, et al. Learning multi-grained aspect target sequence for Chinese sentiment analysis[J]. Knowledge-Based Systems, 2018, 148: 167-176.
[13] CHE W X, ZHAO Y Y, GUO H L, et al. Sentence com-pression for aspect-based sentiment analysis[J]. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2015, 23(12): 2111-2124.
[14] LI S, ZHAO Z, HU R F, et al. Analogical reasoning on Chinese morphological and semantic relations[C]//Proceedings of the 56th Annual Meeting of the Association for Com-putational Linguistics, Melbourne, Jul 15-20, 2018. Strou-dsburg: ACL, 2018: 138-143.
[15] WANG X, LIU Y C, SUN C J, et al. Predicting polarities of Tweets by composing word embeddings with long short-term memory[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Beijing, Jul 26-31, 2015. Stroudsburg: ACL, 2015: 1343-1353.
[16] AUGENSTEIN I, ROCKT?SCHEL T, VLACHOS A, et al. Stance detection with bidirectional conditional encoding[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Nov 1-4, 2016. Stroudsburg: ACL, 2016: 876-885.
[17] TANG D Y, QIN B, LIU T. Aspect level sentiment class-ification with deep memory network[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Nov 1-4, 2016. Stroudsburg: ACL, 2016: 214-224.
[18] MA D H, LI S J, ZHANG X D, et al. Interactive attention networks for aspect-level sentiment classification[C]//Pro-ceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Aug 19-25, 2017: 4068-4074.